testrls.m

来自「卡尔曼滤波器设计的一个例子」· M 代码 · 共 40 行

% RLS used in a simple system identification application.
% By the end of this script the adaptive filter w 
% should have the same coefficients as the unknown filter h.
clear all;
iter = 5000;                  % Number of samples to process
% Complex unknown impulse response
h    = [.9 + i*.4; 0.7+ i*.2; .5; .3+i*.1; .1];     
xn   = 2*(rand(iter,1)-0.5);  % Input signal, zero mean random.
%xn   = filter(.4, [1 -.6], xn);
% although xn is real, dn will be complex since h is complex
dn   = filter(h,1,xn);        % Unknown filter output 
en   = zeros(iter,1);         % vector to collect the error

% Initialize RLS with a filter of 10 coef.
[w,x,d,y,e,R]=init_rls(10,0.1); 

%% Processing Loop
for (m=1:iter)
   % update the input delay line
   x = [xn(m,:); x(1:end-1,:)];  
   d = dn(m,:) + 1e-3*rand;      % additive noise of var = 1e-6   
   % call RLS to calculate the filter output, estimation error
   % and update the filter coefficients. 
   [w,y,e,R]=asptrls(x,w,d,R,0.97);

   % save the last error sample to plot later
   en(m,:) = e;  

end;

% display the results
subplot(2,2,1);stem([real(w) imag(conj(w))]); grid;
xlabel('filter after convergence')
subplot(2,2,2);
eb = filter(0.1,[1 -.9], en .* conj(en));
plot(10*log10(eb  ));grid
axis([0 5000 -80 0]);
ylabel('estimation error [dB]')
xlabel('Learning curve')